Businesses have a desire to be able to tailor advertising, promotions and offers to individual customers to increase the likelihood of engaging their interest and making a sale. Businesses are also interested in identifying people who are not likely customers so that they can avoid wasting time and money on them. Since it is typically impractical to gather enough data about a potential customer to make customization completely individual, the typical proxy is to partition the customer space into some discrete, usually small, number of market segments. The individual customer is then classified or categorized into a pre-defined market segment that seems to have a best fit.
As an example of such segmentation, a manufacturer might divide people into categories such as “price conscious” vs. “novelty seeking” vs. “brand loyal” vs. “status conscious” vs. “concerned with the environment” vs. “likes to think they are getting the best of you.” The categorization is made for the purposes of selecting a particular direct mail (physical mail or e-mail) pitch to send to potential customers. A department store or web site, on the other hand, might be concerned with deciding to which department the customer should be steered.
Typically, information is obtained about a customer by looking at past behavior of that customer, either when interacting with this particular party, or from information purchased from third parties. The information might be valid, but there is always a question of how representative the information is and how much predictive power it has. There is an additional problem in that the identity of a customer is assumed, or at least a stable proxy for the customer. When customers are anonymous, there is no way to glean information about them. There is also a problem categorizing new customers, for whom no information yet exists.
An alternative way to obtain information is to have the customer fill out a questionnaire. This is time consuming and often seen as intrusive. It is also unlikely to reveal true answers, which may or may not be a problem, depending on the assumptions used in creating the rules.
The main conflict is that on one hand, customers are justifiably wary about giving out personal information and about businesses knowing too much about them. On the other hand, customers appreciate it when customization works and businesses correctly determine what a customer will find interesting and avoid showing the customer things that will not be found interesting.
It is desirable for users of e-commerce to maintain privacy of personal data while also receiving targeted offers of interest. Data mining past interaction behavior with e-commerce web sites is error-prone, intrusive and foiled when a user chooses to remain anonymous. Further, mining of past interaction may not necessarily be predictive of future interactions.
In some cases, customers are asked to classify themselves. This method lacks uniformity, even when the customer is provided explanation. There is no way to match these self-classifications to actual learned rules.